Smart Detection and Prevention of Phishing Websites Using ML & NLP

Phishing Website Detection using Artificial Neural Networks and Count Vectorization: A Web-Deployed Approach

A Hybrid Machine Learning and Rule-Based Approach for Phishing Website Detection Using URL Features

The model proposed employs feature extraction using URLs, such as lexical and structural features like URL length, frequency of special characters, use of IP addresses, and occurrence of suspicious keywords, to improve the accuracy and reliability of detection.

Comparison of the Performance of Machine Learning Classification Algorithms on Phishing URL Detection

The development of internet technology has increased the intensity of digital activities, but it has also been followed by an increase in cybersecurity threats, one of which is phishing attacks through malicious URLs. Phishing is a fraudulent method that is carried out by manipulating users through fake websites that resemble official websites to obtain sensitive information, such as usernames, passwords, and financial data. Conventional blacklist-based detection methods are considered less effective in recognizing new phishing URLs that continue to develop dynamically. Therefore, this study aims to analyze and compare the performance of various machine learning and deep learning algorithms in accurately detecting phishing URLs. The dataset used was obtained from Kaggle with a total of 11,054 data points and 31 features that represent the characteristics of phishing and legitimate URLs. The methods used include Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest, Gradient Boosting, CatBoost, Extreme Gradient Boosting, and Multilayer Perceptron. The research stages include data preprocessing, exploratory data analysis, data visualization, separation of training and testing data, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The results showed that the Gradient Boosting algorithm provided the best performance with an accuracy of 0.974, an F1-score of 0.977, a recall of 0.994, and a precision of 0.986. The results show that the ensemble learning method is able to detect phishing URLs effectively and can be used to improve artificial intelligence-based cybersecurity systems.

Transformer-based Phishing Website Detection and Cyber Attack Prevention Systems

Phishing websites still pose a threat to internet users by using well-known domain names and confusing URL formats to trick them into divulging confidential information. This research proposes a phishing website detection system and a cyberattack prevention system based on a deep learning model designed to detect whether raw URLs are phishing or legitimate, leveraging a transformer model. The system analyzes URL sequences to identify patterns, domain name irregularities, suspicious tokens, unusual lengths, special character usage, and deceptive subdomains. A web interface enables the user to input URLs, and the Flask backend performs pre-processing, tokenization, model inference, and suspicious feature identification. The proposed method fuses the transformer-based semantic representation of the URL and lexical feature analysis to reliably detect phishing and provide explainable warning features. The system creates a prediction label, confidence score, and suspicious features in real time to support the decision-making process. This work provides a practical and scalable solution for phishing identification, user protection, and web-based cyberattack prevention.

Phishing Website Detection Using A Stacked Hybrid Model With Explainable AI

A Phishing Website Detection System Using a Stacked Hybrid Model With Explainable AI, designed to accurately classify websites as phishing or legitimate while providing transparent decision explanations, is presented.

Phishing Website Detection Using Machine Learning

Abstract— Phishing website detection using machine learning focuses on the design and implementation of an intelligent system for detecting malicious URLs using machine learning techniques. The system aims to classify URLs as either legitimate or malicious by analyzing various structural and statistical features extracted from the URLs. A dataset containing both benign and malicious URLs is used to train and evaluate the model. The proposed approach utilizes a Gradient Boosting Classifier due to its high accuracy and ability to handle complex patterns in data. Feature extraction plays a crucial role in the project, where attributes such as URL length, presence of special characters, domain age, use of HTTPS, and abnormal patterns are considered. These features are fed into the model, which learns to differentiate between safe and harmful URLs. The project involves several stages, including data collection, preprocessing, feature extraction, model training, and performance evaluation. Multiple machine learning algorithms such as Support Vector Machine (SVM), Decision Tree, Random Forest, and XG Boost are also explored and compared to identify the most effective model .The system is designed to work in real time, allowing users to input URLs and receive instant predictions regarding their safety. Keywords— Phishing Website Detection, Machine Learning, Gradient Boosting Classifier, URL Feature Extraction, Cybersecurity, Malicious URL Detection, Web Security, Classification, Feature Engineering, Real-Time Detection.